Thomas Schön and Antônio Ribeiro, both WASP researchers, and WASP alumn Daniel Gedon, all from Uppsala University, have received the prestigious Automatica Best Paper Award for their paper Deep Networks for System Identification: A Survey.

The award recognizes outstanding contributions to the theory and practice of control engineering or control science, and is presented at the IFAC World Congress to three papers published in Automatica during the preceding three years.

In the award-winning paper, the researchers explore how deep learning can be used for system identification, a field focused on learning mathematical models of dynamic systems from data. The paper explains how deep learning can be used for learning mathematical models of dynamic systems from data. It provides both practical and theoretical insights into contemporary neural network architectures and discusses both practical and theoretical aspects of using deep learning for prediction and modelling.

“It is a great honor to receive this award. The recognition highlights the growing importance of connecting modern AI methods with system identification and motivates us to continue developing reliable machine learning tools for understanding how complex dynamical systems evolve over time. Such methods have the potential to advance research across many domains, including biology and medicine, where improved dynamical models can contribute to more personalized and effective healthcare,” says WASP professor Thomas Schön.